In order to improve the fatigue performance of a welded A-type frame in a heavy off-road mining truck, a novel method was presented to implement lifetime and weight collaborative optimization while considering uncertainties in geometry dimension, material properties, and bearing load. The mechanical and cyclic material parameters were obtained from experimental work to characterize the base metal and the weldment. The finite element model of a welded A-type frame was constructed to analyze stress distribution and predict fatigue life, the force time histories of which were acquired from multi-body dynamics simulation. The simulated failure position and fatigue life had a good agreement with the actual results. Then, both structural lifetime and weight were considered as optimization objectives. The thickness of main steel plates and elastic and cyclic material parameters were chosen as uncertain design variables as well as main loads at connection locations. The fifty sample points in the light of Latin hypercube sampling method and its responses calculated by finite element analysis were supposed to build the approximation model based on the Kriging approximation method. After its fitting precision was guaranteed, the non-dominated sorting genetic algorithm II (NSGA-II) was utilized to find the optimal solution. Finally, the fatigue life of a welded A-type frame was increased to 2.40 × 10⁵ cycles and its mass was lessened by 8.2%. The optimized results implied that good fatigue performance of this welded A-type frame needs better welding quality, lower running speed for downhill and turning road surface, and thicker front plates.

Pre-loading on engineering materials or structures may produce pre-strain, especially plastic strain, which would change the fatigue failure mechanism during their service time. In this paper, an energy-based method for fatigue life prediction on high-strength-steel welded joints under different pre-strain levels was presented. Tensile pre-strain at three pre-strain levels of 0.2%, 0.35% and 0.5% was performed on the specimens of the material Q345, and the cyclic stress and strain responses with pre-loading were compared with those without pre-loading at the same strain level. The experimental work showed that the plastic strain energy density of pre-strained welded joints was enlarged, while the elastic strain energy density of pre-strained welded joints was reduced. Then, based on the strain energy density method, a fatigue life estimation model of the high-strength-steel welded joints in consideration of pre-straining was proposed. The predicted results agreed well with the test data. Finally, the validity of the developed model was verified by the experimental data from TWIP steel Fe-18 Mn and complex-phase steel CP800.

The traditional methodologies for fatigue life assessment of welded joints strongly depend on geometries and surface characteristics, as well as time. In this paper, an energy-based approach, independent of structures though thermal-graphic measurement, was presented to predict life expectancy of welded joints, via limited number of tests. In order to eliminate the thermal elastic effect caused by the welding residual stress, annealing was first conducted on welded specimens. Both monotonic and cyclic tests for welded joints were implemented. Then, based on the thermal evolution of welded joints measured by the quantitative thermo-graphic method, an energy-based approach, taking the linear temperature evolution and the intrinsic dissipation into account, was employed on the fatigue life prediction of flat butt-welded joints. The estimated results showed good agreement with the experimental ones, and the energy tolerance to failure $E_c$ for different stress amplitudes was found to be constant.

A large number of sample data is needed to ascertain the characteristic parameters of traditional membership function, so that the calculated fuzzy fatigue reliability based on this method has certain errors for engineering structures without enough samples. A fuzzy fatigue reliability analysis method based on self-configuring membership function is proposed, while considering its multi-source uncertainties in the design, manufacture, and use stage in order to accurately evaluate fatigue reliability of welded A-type frame. In this paper, a novel membership function was presented on account of a small amount of sample data, which some experimental results verified. The mathematical expression for failure probability was deduced from the suggested model, as well as fatigue reliability. Subsequently, the thickness of steel plate defined in design stage, the material properties of weld metal that is produced in manufacture stage, and the loads at different connection sites determined in use stage were all considered as the random variables, which were obtained from Latin hypercube sampling, and the fatigue limit of weld metal was deemed as the fuzzy variable. Based on the response surface method, the fuzzy fatigue reliability performance function was constructed to assess failure probability of welded A-type frame under the condition of downhill and turning braking with full load, while its fatigue reliability was found to be far less than 90%. The fuzzy fatigue reliability optimization that was based on genetic algorithm was implemented, which showed that its reliability varied from 69.47% to 95.12%.